Systems and methods for progressive semantic mapping

ABSTRACT

Systems, methods, and non-transitory computer-readable media can determine map information defining a map, wherein the map comprises a plurality of regions. A quality level is assigned to each region of the plurality of regions based on map information available for that region. The quality level is associated with at least one of: a resolution metric, a volume metric, a recency metric, a verification metric, or an elegance metric associated with the map information available for that region. A first region of the plurality of regions is identified that is at risk of being downgraded to a lower quality level. Instructions are issued to one or more vehicles that cause the one or more vehicles to traverse the first region and capture sensor data within the first region.

FIELD OF THE INVENTION

The present technology relates to vehicle systems and navigationsystems. More particularly, the present technology relates to systems,apparatus, and methods for progressively updating map data based oninput data obtained from various sources.

BACKGROUND

Vehicles are increasingly being equipped with intelligent features thatallow them to monitor their surroundings and make informed decisions onhow to react. Such vehicles, whether autonomously, semi-autonomously, ormanually driven, may be capable of sensing their environment andnavigating with little or no human input as appropriate. The vehicle mayinclude a variety of systems and subsystems for enabling the vehicle todetermine its surroundings so that it may safely navigate to targetdestinations or assist a human driver, if one is present, with doing thesame. As one example, the vehicle may have a computing system (e.g., oneor more central processing units, graphical processing units, memory,storage, etc.) for controlling various operations of the vehicle, suchas driving and navigating. To that end, the computing system may processdata from one or more sensors. For example, a vehicle may have sensorsthat can recognize hazards, roads, lane markings, traffic signals, andthe like. Data from sensors may be used to, for example, safely drivethe vehicle, activate certain safety features (e.g., automatic braking),and generate alerts about potential hazards.

SUMMARY

Various embodiments of the present technology can include systems,methods, and non-transitory computer readable media configured todetermine map information defining a map, wherein the map comprises aplurality of regions. A quality level is assigned to each region of theplurality of regions based on map information available for that region.The quality level is associated with at least one of: a resolutionmetric, a volume metric, a recency metric, a verification metric, or anelegance metric associated with the map information available for thatregion. A first region of the plurality of regions is identified that isat risk of being downgraded to a lower quality level. Instructions areissued to one or more vehicles that cause the one or more vehicles totraverse the first region and capture sensor data within the firstregion.

In an embodiment, input data pertaining to the first region is received.Map information associated with the first region is updated based on theinput data.

In an embodiment, the quality level for the first region is updatedbased on the updated map information.

In an embodiment, the input data comprises sensor data captured by oneor more sensors mounted to the one or more vehicles while drivingthrough the first region.

In an embodiment, updated map information is transmitted to one or morevehicles based on the updating the map information associated with thefirst region.

In an embodiment, the first region is assigned a first quality level ofa plurality of quality levels. The first quality level is indicative ofAV-quality map information that would permit operation of an autonomousvehicle within the first region.

In an embodiment, a rideshare request is assigned to an autonomousvehicle based on the first region being assigned the first quality levelof the plurality of quality levels.

In an embodiment, issuing instructions to the one or more vehicles thatcause the one or more vehicles to traverse the first region and capturesensor data within the first region comprises issuing instructions thatcause one or more autonomous vehicles to traverse the first region andcapture sensor data within the first region.

In an embodiment, input data pertaining to the first region is received.The input data is indicative of a change to one or more map elementswithin the first region. The quality level assigned to the first regionis downgraded based on the input data.

In an embodiment, assigning the quality level to each region of theplurality of regions based on map information available for that regioncomprises assigning one quality level of a plurality of quality levelsto each region of the plurality of regions. Each quality level of theplurality of quality levels is associated with a set of qualitycriteria. Each region is assigned the highest quality level for whichthe associated set of quality criteria is satisfied.

It should be appreciated that many other features, applications,embodiments, and variations of the disclosed technology will be apparentfrom the accompanying drawings and from the following detaileddescription. Additional and alternative implementations of thestructures, systems, non-transitory computer readable media, and methodsdescribed herein can be employed without departing from the principlesof the disclosed technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B illustrate example situations demonstrating variouschallenges that may be experienced in conventional approaches to mapgeneration.

FIG. 2 illustrates an example environment including a transportationmanagement system, according to an embodiment of the present technology.

FIGS. 3A-C illustrate an example situation demonstrating verification ofsemantic map information, according to an embodiment of the presenttechnology.

FIG. 4A illustrates example map information quality levels, according toan embodiment of the present technology.

FIG. 4B illustrates example map information, according to an embodimentof the present technology.

FIG. 5 illustrates an example method, according to an embodiment of thepresent technology.

FIG. 6 illustrates an example block diagram of a transportationmanagement environment, according to an embodiment of the presenttechnology.

FIG. 7 illustrates an example of a computer system or computing devicethat can be utilized in various scenarios, according to an embodiment ofthe present technology.

The figures depict various embodiments of the disclosed technology forpurposes of illustration only, wherein the figures use like referencenumerals to identify like elements. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated in the figures can be employedwithout departing from the principles of the disclosed technologydescribed herein.

DETAILED DESCRIPTION

Vehicles are increasingly being equipped with intelligent features thatallow them to monitor their surroundings and make informed decisions onhow to react. Such vehicles, whether autonomously, semi-autonomously, ormanually driven, may be capable of sensing their environment andnavigating with little or no human input. The vehicle may include avariety of systems and subsystems for enabling the vehicle to determineits surroundings so that it may safely navigate to target destinationsor assist a human driver, if one is present, with doing the same. As oneexample, the vehicle may have a computing system for controlling variousoperations of the vehicle, such as driving and navigating. To that end,the computing system may process data from one or more sensors. Forexample, a vehicle may have one or more sensors or sensor systems thatcan recognize hazards, roads, lane markings, traffic signals, etc. Datafrom sensors may be used to, for example, safely drive the vehicle,activate certain safety features (e.g., automatic braking), and generatealerts about potential hazards.

Mapping is also fundamental to operating a vehicle autonomously orsemi-autonomously. However, being able to collect maps at scale iscostly, technically challenging, and requires heavy capital investment.Furthermore, the delta between a simple map used for consumerapplications and a map needed for an autonomous vehicle is large andrequires a significant increase in investment and capability to deliver.FIG. 1A illustrates an example of a simple map 100 that may be used forconsumer applications. The simple map 100 may be two-dimensional, andmay include basic positions and directions of roads in a geographicregion. However, such simple maps do not contain sufficient informationfor autonomous or semi-autonomous vehicle navigation.

For autonomous or semi-autonomous navigation, vehicles may rely oncomplex three-dimensional maps that contain very precise geometric andsemantic information. For example, an AV-quality map of a geographicregion can include geometric information pertaining to geometricfeatures (e.g., physical features) that correspond to the geographicregion. Such geometric information may include, for example, positionsand/or shapes of physical structures or objects or other physicalfeatures in a geographic region. For example, FIG. 1B illustrates anexample environment 120 being traveled by a vehicle 122 while navigatinga road segment 124. An AV-quality map may include three-dimensionalgeometric information that identifies various physical features (e.g.,trees 136, road signs 132, 134, crosswalk 128, etc.) and their positionswithin the environment 100.

In order to perform autonomous or semi-autonomous navigation, a vehiclerequires information not only about the geometric, physical features inan area, but also contextual (or semantic) information about thosephysical features. For example, in the example shown in FIG. 1B, anAV-quality map may include geometric information which indicates theposition of the road segment 124 and/or the positions of variousphysical features, such as the trees 136 or signs 132, 134 proximate theroad segment 124. However, in order to autonomously navigate the roadsegment 124, the vehicle 122 also needs to know that the road segment124 has two lanes 130 a, 130 b, the precise positions of boundary lines136 a-c defining the lanes 130 a, 130 b, the direction of travel in eachlane 130 a, 130 b, the speed limit of the road segment 124, the factthat a crosswalk 128 crosses the road segment 124, the precise positionof the crosswalk 128, and the like. However, generating such detailedthree-dimensional AV-quality maps that are suitable for use inautonomous or semi-autonomous navigation is extremely costly, timeintensive, and technically challenging. There is currently a lack ofavailable solutions for generating such detailed, precise AV-qualitymaps in a scalable, efficient manner that are not purely capitalintensive. Conventional approaches pose disadvantages in addressingthese and other problems.

An improved approach in accordance with the present technology overcomesthe foregoing and other disadvantages associated with conventionalapproaches. In various embodiments, the present technology provides aholistic pipeline that progressively builds a higher quality map withthe ingestion of richer and/or more voluminous input data or data atsome “sufficient” volume. In an embodiment, such input data may bereceived from users (e.g., a computing device associated with the user)and/or vehicles (e.g., one or more sensors on the vehicle) traversing ageographic region. For example, at least some of the input data may bereceived from drivers, riders, and/or vehicles traversing certaingeographic regions as part of a ridesharing service. Map informationdefining a map may be generated and updated based on the input data. Themap may comprise varying degrees of quality in different regions of themap (e.g., based on the types of sensors that were used to collect datain each region). For example, certain regions of the map may comprise alowest level of quality, certain regions of the map may comprise ahighest level of quality (e.g., AV-quality), and other regions of themap may comprise one or more intermediate levels of quality. In anembodiment, each region of the map may be assigned a quality level(e.g., a quality score) indicative of the quality of the map informationavailable for that region based on various quality criteria. In anembodiment, base map information may be received which defines a lowestquality level in the map. For example, the base map information maycomprise two-dimensional map information with sufficient data fornavigation routing and/or ETA calculation. The base map information maybe suitable, for example, for providing driving instructions to a humandriver but may be unsuitable for autonomous or semiautonomousnavigation.

Over time, as input data pertaining to a geographic region is received,the map information for that region may be augmented and improved usingthe additional input data. In various embodiments, the progressiveingestion of input data may have a step function nature where keyelements are added to the map using automated approaches. Map features,such as lanes, lane markings, stop signs, crosswalks, traffic lights,and other semantic information can be progressively added to the mapinformation and their accuracy improved over time as more (and/orsufficient) input data is received. In an embodiment, when a section orregion of a map is assigned a particular quality level based onsatisfaction of certain quality criteria, the present technology canautomatically schedule and/or queue up appropriate resources forachieving a next set of quality criteria in order to upgrade that regionto a higher quality level. This may include, for example, automatingdata collection in specific areas to fill in any missing gaps,instructing vehicles with varying levels of sensor suites (e.g.,autonomous, human-driven, etc.) for high quality data collection,generating projects for human operators to curate sections of the map toAV quality, generating simulations to evaluate map information in aregion, and the like. This process may continue until a region reachesAV quality, at which point the region may be marked as such (e.g.,assigned a quality level or quality score indicative of AV quality).Regions that are associated with AV-quality map information may then beunlocked for autonomous vehicles to operate (e.g., within an operationaldesign domain (ODD) commensurate with the relative AV quality score ofthe available map routes). Map information can continuously be updated,and appropriate actions can be scheduled and/or queued based on updatedinput data as it is received. In various embodiments, geographicregions, as discussed herein, may comprise a single road segment, aportion of a road segment, or multiple road segments. More detailsrelating to the present technology are provided below.

FIG. 2 illustrates an example environment 200, according to anembodiment of the present technology. The environment 200 can include anexample transportation management system 202 and a fleet of vehicles212. The fleet of vehicles 212 may be managed by the transportationmanagement system 202. The transportation management system 202 can be,for example, a transportation management system 660 of FIG. 6. The fleetof vehicles 212 can be, for example, a fleet comprised of one or morevehicles, such as a vehicle 640 of FIG. 6. As shown, the transportationmanagement system 202 can include an information collection module 204,an information verification module 206, a map information module 208,and a map information utilization module 210. In various embodiments,the transportation management system 202 can access sensor datacollected by sensors of the fleet of vehicles 212 from various sourcesand geographic locations. Sensor data may be collected by sensorsmounted to the vehicles themselves and/or sensors on computing devicesassociated with users riding within the fleet of vehicles 212 (e.g.,user mobile devices). For example, the transportation management system202 can access sensor data from the fleet of vehicles 212 in real-time(or near real-time) over one or more computer networks. In anotherexample, the transportation management system 202 can be configured tocommunicate and operate with at least one data store 220 that isaccessible to the transportation management system 202. The at least onedata store 220 can be configured to store and maintain various types ofdata, such as sensor data captured by the fleet of vehicles 212, mapinformation, and the like. In general, sensor data captured by the fleetof vehicles 212 (e.g., point cloud data, image data, video data,acoustic data, etc.) can provide geometric and/or semantic informationpertaining to geographic locations. In some embodiments, some or all ofthe functionality performed by the transportation management system 202and its sub-modules may be performed by one or more computing systemsimplemented in a vehicle, such as the vehicle 640 of FIG. 6. In someembodiments, some or all of the functionality performed by thetransportation management system 202 and its sub-modules may beperformed by one or more computing systems associated with (e.g.,carried by) one or more users riding in a vehicle and/or participatingin a ridesharing service, such as the computing device 630 of FIG. 6.The components (e.g., modules, elements, etc.) shown in this figure andall figures herein are exemplary only, and other implementations mayinclude additional, fewer, integrated, or different components. Somecomponents may not be shown so as not to obscure relevant details.

The information collection module 204 can be configured to receive basemap information. As will be discussed in greater detail herein, base mapinformation may define a lowest quality level for a map generated andmaintained using the presently disclosed technology. In an embodiment,base map information may comprise sufficient information to providebasic navigation or travel directions to a human driver, but maycomprise insufficient information for autonomous navigation. Forexample, base map information may comprise a two-dimensional map that isutilized to provide navigation guidance and route guidance to drivers ina ridesharing service. In certain embodiments, base map information maycomprise public transit information (e.g., public transit stop locationsand schedules) that can be used to provide navigation guidance and/orroute guidance for users utilizing a public transit system.

The information collection module 204 can be further configured toreceive input data for updating and/or improving map information. In anembodiment, the input data can comprise sensor data captured by one ormore sensors or map data provided by third party sources. In certainembodiments, sensor data can comprise sensor data captured by one ormore user computing devices (e.g., user mobile devices). For example,drivers and/or riders participating in a ridesharing service may carryone or more user computing devices. Such user computing devices maycomprise cameras that can capture image and/or video data, GPS receiversthat can capture location data, one or more microphones that can captureaudio data, and the like. As such, input data can comprise image data,video data, audio data, location data, or any other data that may becaptured using sensors on user computing devices.

In certain embodiments, sensor data can comprise sensor data captured byone or more sensors mounted to a vehicle (e.g., the fleet of vehicles212). For example, certain vehicles, whether manually, autonomously, orsemi-autonomously operated, may comprise one or more sensors mounted tothe vehicle. Such sensors may include lidar systems that can capturelidar data, radar systems that can capture radar data, camera systemsthat can capture image and/or video data, microphones that can captureaudio data, positioning systems that can capture location data, and thelike. As such, input data can comprise lidar data, radar data, imagedata, video data, audio data, location data, or any other data that maybe captured using sensors mounted on a vehicle. As will be described ingreater detail below, input data received by the information collectionmodule 204 can be used to determine a quality level for various regionsin a map, and can also be used to determine any missing or requiredinformation that may be needed to upgrade a map region to a higherquality level.

In certain embodiments, the information collection module 204 can beconfigured to make various inferences or draw conclusions based on theinput data received by the information collection module 204. Suchinferences may include, for example, the approximate positions ofphysical objects and/or features in a geographic region. For example,consider an example situation in which a ridesharing service driver'smobile device is used to capture sensor data as the driver drivesthrough a particular geographic region. An image captured by thedriver's mobile device may include a stop sign. A machine learning model(e.g., an object recognition model) can recognize that the image depictsa stop sign. Furthermore, location data indicative of the location ofthe mobile device at the time the image was captured can be used toestimate the location of the stop sign within the geographic region, andmap information can be updated to indicate the approximate position ofthe stop sign within a map. In this way, input data received by theinformation collection module 204 can be used to add information to amap or improve information in the map. Similarly, consider a furtherexample situation in which a vehicle comprises a sensor suite with moreaccurate, higher resolution sensors, such as high definition cameras,lidar systems, radar systems, and the like. The vehicle can drivethrough the same geographic region, and capture image data, lidar data,and/or radar data indicating the position of the same stop sign withgreater accuracy than was possible using only the previous driver'smobile device. As such, using the sensor data captured by the vehicle,map information can be updated with an even more accurate estimate ofthe position of the stop sign within the map.

The information verification module 206 can be configured to providefeatures and/or tools to verify map information. In various embodiments,verification of map information may be performed by human operatorsand/or automatically performed by machine learning models. As mentionedabove, the information collection module 204 can receive input data.Such input data can comprise base map information and/or sensorinformation captured by various sensors. Furthermore, the informationcollection module 204 may make various inferences based on the inputdata and map information can be updated based on those inferences. Suchinferences may comprise, for example, geometric information indicativeof the physical locations of physical features within a map (e.g., thepositions of traffic lights, crosswalks, road signs, lane boundaries,other road markings, buildings, and the like). Furthermore, suchinferences may comprise semantic information providing further contextpertaining to the physical features identified in the geometricinformation. For example, semantic information may include the directionof travel in a particular lane, the speed limit for a particular roadsegment, and the like. The information verification module 206 can beconfigured to confirm the accuracy of available map information. Forexample, a human operator may be provided with a user interface thatallows the human operator to view and confirm the accuracy of mapinformation (e.g., geometric and/or semantic information) and/or toconfirm the accuracy of machine learning models that have lowconfidence.

FIGS. 3A-3C illustrate an example situation that demonstratesverification of map information, according to an embodiment of thepresent technology. FIG. 3A depicts an image 300. The image 300 may havebeen captured, for example, by a camera mounted to a vehicle while alidar system mounted to the vehicle captured lidar data that was used togenerate a point cloud and three-dimensional geometric information for ageographic region. The image 300 may be associated with image locationdata which can be used to determine a geographic location from which theimage was captured. The image 300 may also be associated with camerainformation describing an angle of view of the camera that captured thefirst image, the position, orientation, and/or direction of the camerawhen the first image was captured, and the like. The image 300 depictslane markers 312, 314, 316, 318, 320 which define two bike lanes 306,308 and two road lanes 302, 304.

As discussed above, map information can be inferred based on sensordata. For example, in the example situation depicted, sensor data (e.g.,image data, lidar data) can be used to infer and/or estimate thepositions of the lane markers 312, 314, 316, 318, 320 within a map suchthat map information may comprise the estimated positions of the lanemarkers 312, 314, 316, 318, 320.

In FIG. 3B, the estimated positions of the lane markers 312, 314, 316,318, 320 contained within the map information have been overlaid on theimage using overlays 340, 338, 336, 334, 332. For example, if theapproximate position from which the image 300 was captured can bedetermined within the map, and the orientation of the camera can bedetermined, and the map information comprises estimated positions forthe lane markers 312, 314, 316, 318, 320, an estimate can be made as towhere those lane markers would be depicted in the image 300. However, inthe situation shown in FIG. 3B, the positions of the overlays 340, 338,336, 334, 332 are not very accurate, indicating that the map informationis not particularly accurate. The human operator can be provided with anoption to indicate whether the semantic information depicted in theimage 300 (e.g., the positions of the overlays 340, 338, 336, 334, 332)is accurate or inaccurate. In an embodiment, the human operator can alsobe provided with the option to correct any incorrectly positionedsemantic information. In certain embodiments, confirmation and/orcorrection of semantic information may be automated by using multipleobservations of the same object across multiple devices. For example, ifthe same lane marker is seen ten times by ten different vehicles, itwill likely have a geospatial position that is more accurate than if ithad been observed only once. The number of observations may obviate theneed for humans to confirm, or may place the object more accurately thana human could. In FIG. 3C, the semantic information is accuratelypositioned over the lane markers. As such, the operator may indicatethat this portion of the map information is accurate and has beenverified by a human operator. While the example of lane markerpositioning has been demonstrated here, it should be understood that anysemantic information or 3D representation of a map element contained inmap information can be verified by a human operator and/or a machinelearning model. For example, the image 300 may demonstrate the directionof travel of a particular lane, and/or the speed limit of the lane, orthe position of a crosswalk or traffic signal, and the human operatorcan indicate whether the information is accurate.

In certain embodiments, verification of map information may be performedautomatically by one or more machine learning models. For example, amodel may be trained to identify the likely positions of variousphysical features in an image, and to determine whether semanticinformation overlaid on the image accurately identifies the positions ofthe physical features depicted in the image. As will be described ingreater detail below with reference to the map information module 208,verification of map information can be utilized to determine whether mapinformation is sufficiently accurate to be assigned a particular qualitylevel (e.g., a particular quality score), or whether additionalinformation is needed to improve the accuracy of the map information.

Returning to FIG. 2, the map information module 208 can be configured tomaintain and update map information based on input data. As mentionedabove, map information can define a map of a geographic region. In anembodiment, portions and/or regions of a map and/or individual mapelements can be assigned a quality level based on the quality ofinformation available for that region. The map information can includethe quality level information for the various regions of the map. Forexample, if a particular region of a map has only base map information(e.g., because no sensor data is available for that region), that regionmay be assigned a lowest quality level (e.g., a lowest quality score).In contrast, if a particular region of the map has sufficiently accurateand detailed information such that autonomous vehicles can safelyoperate in that region based on the available map information, theregion may be assigned a highest quality level (e.g., a highest qualityscore). In certain embodiments, a highest quality level may beassociated not only with safe operation of autonomous vehicles, but alsosafe operation with “high elegance,” i.e., safe operation while alsotaking into account various rider comfort considerations, as will bedescribed in greater detail herein. One or more intermediate qualitylevels may be defined between the lowest quality level and the highestquality level.

Each quality level may be associated with one or more quality criteriawhich define the minimum requirements for a map region to be assignedthat quality level. Quality criteria may comprise data resolutioncriteria, data type criteria, data coverage criteria, data volumecriteria, data accuracy criteria, information verification criteria,test result criteria, information freshness criteria, and the like,various examples of which will be described below. For example, consideran example situation in which five quality levels are defined. FIG. 4Aillustrates an example set of five quality levels. A lowest qualitylevel may be associated with base level map information, and may requireonly that base map information is available for a region. A secondquality level may require that some level of sensor data is availablethroughout a region (e.g., for a threshold percentage of the region).For example, the second quality level may require that sensor data fromuser mobile devices and/or from vehicles, such as camera image dataand/or GPS trace data, is available for a threshold percentage of theregion.

A third quality level may require that high quality sensor data isavailable throughout a region (e.g., for a threshold percentage of theregion). For example, the third quality level may require, at a minimum,that three-dimensional lidar data and high definition image data areavailable for a threshold percentage of the region. Furthermore, thethird quality level may comprise information freshness criteriarequiring that sensor data for the region must have been captured withina threshold period of time (e.g., within the last week, the last month,etc.). The third quality level may also be associated with and/orrequire availability of scenario information for a geographic region. Inthis regard, the geographic region or road segments thereof can beassociated with various scenarios. Scenario information associated witha geographic region can include information describing objects, events,context, and risk associated with navigation through the geographicregion. For example, scenarios can include children walking through acrosswalk, pedestrians crossing a road, debris blocking a lane of ahighway, hazardous activity involving other vehicles, to name someexamples. A scenario can be associated with a set of features (e.g.,objects, road features, contextual features) which, when detected, canbe used to recognize the scenario. Scenario information can be used toinform appropriate action, such as route planning, navigationdecision-making, area avoidance, and the like.

A fourth quality level may require that the accuracy of map informationin a region has been verified by a human operator and/or a machinelearning model, various example of which were discussed above withreference to the information verification module 206. In this exampleimplementation, the fourth quality level may indicate that there issufficient map information and sufficiently accurate map informationthat an autonomous vehicle could potentially operate using the availablemap information.

A fifth and highest quality level may be associated with AV-quality mapinformation and may indicate that an autonomous vehicle can safelyoperate within a particular region. The fifth quality level may comprisetest result criteria which requires a threshold level of autonomousvehicle testing in the region, and a threshold success rate and/or athreshold safety metric for autonomous vehicles tested in the region.For example, the test result criteria may comprise a minimum number ofautonomous vehicle test drives in the region (e.g., with a humanoperator that can disengage the autonomous vehicle to avoid accidents,or simulated test drives) and a minimum safety metric threshold forthose test drives (e.g., a maximum number of disengagements, or maximumnumber of simulated negative events).

While the example implementation discussed above identifies “AV-qualitymap information” as being associated with a highest quality level, incertain embodiments, a plurality of quality levels may comprise one ormore quality levels above AV-quality map information. For example, inthe example implementation discussed above, the plurality of qualitylevels may comprise a sixth quality level indicative of not onlyAV-quality map information, but AV-quality map information with highelegance which takes into account one or more rider comfortconsiderations. For example, once a particular map region has beendetermined to be safe for autonomous vehicle operation, additionalsensor data can be obtained in order to improve rider comfort whentraveling in autonomous vehicles within the region. For example, G-forceand/or acceleration data can be obtained which can be used to adjustautonomous vehicle performance such that an autonomous vehicle travelingwithin the region can ensure that users do not experience greater than athreshold level of acceleration or a threshold level of G-forces.

While the example implementation above has discussed various types ofinformation and quality criteria associated with particular qualitylevels (e.g., a first, second, third, fourth, and/or fifth qualitylevel), it should be understood that such associations are provided onlyfor purposes of explanation, and are intended as examples rather thanlimitations on the recited features. For example, rather than fivequality levels, more or fewer quality levels may be used. Or rather thana second quality level being associated with sensor data, a first orthird quality level may be associated with sensor data (e.g., havesensor data quality criteria). Each of the example types of informationand the example quality criteria can be applied in various combinationsto different quality levels.

FIG. 4B illustrates an example 400 including an example map 402. The map402 depicts quality level information for various regions within thegeographic region depicted in the map 402. In the example 400, there arefive different quality levels, and each region in the map 402 isassigned a quality level based on the map information available for thatregion, and whether the map information satisfies the quality criteriarequired for a particular quality level. In other embodiments, a map ofa geographic region can depict quality level information for variousregions within the geographic region using a different number of qualitylevels (e.g., three quality levels, six quality levels, etc.).

Returning to FIG. 2, the map information module 208 can be configured toassign each region in a map a quality level based on the map informationavailable for that region. In one embodiment, each quality level may beassociated with a quality score or a range of quality scores. Forexample, in a situation in which there are five quality levels, asdescribed above, the lowest quality level may be assigned a score of 1,the second quality level may be assigned a score of 20, the thirdquality level may be assigned a score of 40, the fourth quality levelmay be assigned a score of 75, and the fifth quality level may beassigned a score of 100. The quality levels and/or the quality scoresmay be indicative of the quality of map information (e.g., completenessand/or accuracy of map information) available for a particulargeographic region. As time passes, and additional input data is receivedand/or map information grows older and potentially stale, the mapinformation module 208 can update (e.g., upgrade or downgrade) thequality level assigned to each region in the map accordingly.

In certain embodiments, the information collection module 204 and/or themap information module 208 may receive input data which indicates achange in a geographic region. For example, a vehicle driving through aregion may capture sensor data that indicates a change to one or moremap elements in the region (e.g., new/removed stop sign, new/removedtraffic light, new crosswalk, changed lane patterns, etc.). Such changesmay cause the map information module 208 to adjust (e.g., downgrade) thequality level of the region to account for the changes to the geographicregion.

In certain embodiments, the map information module 208 can be configuredto transmit updated map information to one or more computing devices(e.g., user computing devices and/or vehicle computing devices). Asdiscussed above, as vehicles and/or users traverse different geographicregions, they collect and provide updated input data (e.g., sensordata). The updated input data can be used to update map information,including updating quality level information for different regions inthe map. As such, map information can be changing dynamically, asdifferent regions can be updated with more accurate information, andupdated information may result in changes to quality levels assigned forparticular regions. The map information module 208 can then push outupdated map information to entities that may rely on map information(e.g., vehicles, drivers, riders) periodically and/or in real-time.

The map information utilization module 210 can be configured to utilizemap information to take various actions. As discussed above, mapinformation can comprise input data (e.g., sensor data) collected forvarious geographic regions, inferences, estimations, and/ordeterminations made based on the input data (e.g., geometric and/orsemantic information inferred, estimated, and/or determined based on theinput data), verification information indicating whether map informationhas been verified by a human operator or a machine model, test resultinformation for different geographic regions, quality level informationfor different geographic regions, and the like. The map informationutilization module 210 can be configured to take various actions basedon this information.

In an embodiment, the map information utilization module 210 can beconfigured to automatically schedule and/or queue one or more tasks inorder to improve the quality level for a region (e.g., to satisfy one ormore quality criteria associated with a higher quality level). Forexample, using the example situation discussed above in which a mapportion may be assigned one of five quality levels, if a particulargeographic region is assigned the lowest quality level (indicative ofonly base level map information being available for that region), themap information utilization module 210 can cause user computing devicesin that region to capture and provide sensor data such that, eventually,the geographic region can be upgraded to the second quality level. If aparticular geographic region is assigned the second quality level(indicative of low-quality sensor data being available for a thresholdpercentage of the geographic region), the map information utilizationmodule 210 can schedule vehicles with high-quality sensors mounted totravel through the geographic region to capture high-quality sensor dataso that the geographic region can be upgraded to the third qualitylevel. If the particular geographic region is assigned the third qualitylevel (indicative of high-quality sensor data being available for athreshold percentage of the geographic region), the map informationutilization module 210 can schedule a human operator to review andverify map information for the geographic region so that the geographicregion can be upgraded to the fourth quality level. If a human operatorreviews the map information and cannot verify its accuracy, the mapinformation utilization module 210 can cause vehicles to be scheduledand/or instructed to travel through the geographic region to captureadditional sensor data in order to improve the accuracy of the availablemap information. If a particular geographic region is assigned thefourth quality level (indicative of verified, highly accurate mapinformation), the map information utilization module 210 can scheduletest drives of autonomous vehicles with human operators orhuman-operated vehicles with AV quality sensor suites through thegeographic region, or schedule simulations of autonomous vehiclesthrough the geographic region, so that the geographic region can beupgraded to the fifth quality level. While various example qualitylevels and associated quality criteria are described herein, it shouldbe understood that the present technology is not limited to the specificexamples provided, and many variations are possible. For example, moreor fewer quality levels can be defined, and each quality level may havedifferent, more, or fewer quality criteria, and the like.

In an embodiment, the map information utilization module 210 can beconfigured to automatically schedule and/or queue one or more tasks inorder to maintain the quality level for a region (e.g., to continue tosatisfy one or more quality criteria associated with the quality levelassigned to the region). For example, a particular quality level may beassociated with a sensor data age threshold (or freshness threshold)such that sensor data must have been captured within a threshold periodof time in order to satisfy the quality criteria for that quality level.If, for a particular region, sensor data is about to exceed the agethreshold such that the region is at risk of being downgraded to a lowerquality level, the map information utilization module 210 canautomatically schedule and/or instruct vehicles with mounted sensors totravel through the geographic region in order to update the sensor dataand keep it up to date. In another example, if a highest quality level(indicative of AV-quality map information) requires a threshold numberof autonomous trips through a region within a threshold period of time,and a particular region is at risk of falling below the threshold numberof autonomous trips, the map information utilization module 210 canautomatically schedule autonomous vehicles to drive through the regionin order to maintain the quality level of the region. Many variationsare possible. In certain embodiments, the map information utilizationmodule 210 may be configured to prioritize certain tasks over othersbased on an order of priority. For example, the map informationutilization module 210 may prioritize scheduling tasks to addresscritical changes in map information (e.g., a new stop sign, a newtraffic light, changed lanes, etc.); a second priority could be mappinga new area to balance traffic considerations, improve ETAs, lowerinsurance costs for covering an area, etc.; a third party could be toaddress time-based decay to “refresh” a geographic area, etc. Manyvariations are possible.

In an embodiment, the map information utilization module 210 can beconfigured to automatically perform ridesharing tasks based on mapinformation, including quality level information. For example, in anembodiment, if a particular region has been identified as havingAV-quality map information, the map information utilization module 210can permit and/or cause autonomous vehicles to be deployed and operatedwithin that region. For example, if a ridesharing request can be carriedout entirely within a geographic region for which AV-quality mapinformation is available, the map information utilization module 210 cancause an autonomous vehicle to be assigned to the ridesharing request.In contrast, if a particular region has been identified as not havingAV-quality map information, the map information utilization module 210can cause autonomous vehicles to not be deployed or operated within thatregion. For example, if a ridesharing request cannot be carried outentirely within a region with AV-quality map information, the mapinformation utilization module 210 can cause a human operated vehicle tobe assigned to the ridesharing request. In yet another example, if aridesharing request can be carried out entirely within a geographicregion for which AV-quality map information is available, but anautonomous route would require a more circuitous route in order to avoidregions for which AV-quality map information is not available, a usermay be provided with a lower fare for accepting an autonomous vehicle.

FIG. 5 illustrates an example method 500, according to an embodiment ofthe present technology. At block 502, the example method 500 candetermine map information defining a map, wherein the map comprises aplurality of regions. At block 504, the example method 500 can assign aquality level for each region of the plurality of regions based on mapinformation available for that region. At block 506, the example method500 can identify a first region of the plurality of regions that is atrisk of being downgraded to a lower quality level. At block 508, theexample method 500 can issue instructions to one or more vehicles thatcause the one or more vehicles to traverse the first region and capturesensor data within the first region.

Many variations to the example method are possible. It should beappreciated that there can be additional, fewer, or alternative stepsperformed in similar or alternative orders, or in parallel, within thescope of the various embodiments discussed herein unless otherwisestated.

FIG. 6 illustrates an example block diagram of a transportationmanagement environment for matching ride requestors with vehicles. Inparticular embodiments, the environment may include various computingentities, such as a user computing device 630 of a user 601 (e.g., aride provider or requestor), a transportation management system 660, avehicle 640, and one or more third-party systems 670. The vehicle 640can be autonomous, semi-autonomous, or manually drivable. The computingentities may be communicatively connected over any suitable network 610.As an example and not by way of limitation, one or more portions ofnetwork 610 may include an ad hoc network, an extranet, a virtualprivate network (VPN), a local area network (LAN), a wireless LAN(WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitanarea network (MAN), a portion of the Internet, a portion of PublicSwitched Telephone Network (PSTN), a cellular network, or a combinationof any of the above. In particular embodiments, any suitable networkarrangement and protocol enabling the computing entities to communicatewith each other may be used. Although FIG. 6 illustrates a single userdevice 630, a single transportation management system 660, a singlevehicle 640, a plurality of third-party systems 670, and a singlenetwork 610, this disclosure contemplates any suitable number of each ofthese entities. As an example and not by way of limitation, the networkenvironment may include multiple users 601, user devices 630,transportation management systems 660, vehicles 640, third-party systems670, and networks 610. In some embodiments, some or all modules shown inFIG. 2 may be implemented by one or more computing systems of thetransportation management system 660. In some embodiments, some or allmodules shown in FIG. 2 may be implemented by one or more computingsystems in the vehicle 640. In some embodiments, some or all modulesshown in FIG. 2 may be implemented by the user device 630.

The user device 630, transportation management system 660, vehicle 640,and third-party system 670 may be communicatively connected orco-located with each other in whole or in part. These computing entitiesmay communicate via different transmission technologies and networktypes. For example, the user device 630 and the vehicle 640 maycommunicate with each other via a cable or short-range wirelesscommunication (e.g., Bluetooth, NFC, WI-FI, etc.), and together they maybe connected to the Internet via a cellular network that is accessibleto either one of the devices (e.g., the user device 630 may be asmartphone with LTE connection). The transportation management system660 and third-party system 670, on the other hand, may be connected tothe Internet via their respective LAN/WLAN networks and Internet ServiceProviders (ISP). FIG. 6 illustrates transmission links 650 that connectuser device 630, vehicle 640, transportation management system 660, andthird-party system 670 to communication network 610. This disclosurecontemplates any suitable transmission links 650, including, e.g., wireconnections (e.g., USB, Lightning, Digital Subscriber Line (DSL) or DataOver Cable Service Interface Specification (DOCSIS)), wirelessconnections (e.g., WI-FI, WiMAX, cellular, satellite, NFC, Bluetooth),optical connections (e.g., Synchronous Optical Networking (SONET),Synchronous Digital Hierarchy (SDH)), any other wireless communicationtechnologies, and any combination thereof. In particular embodiments,one or more links 650 may connect to one or more networks 610, which mayinclude in part, e.g., ad-hoc network, the Intranet, extranet, VPN, LAN,WLAN, WAN, WWAN, MAN, PSTN, a cellular network, a satellite network, orany combination thereof. The computing entities need not necessarily usethe same type of transmission link 650. For example, the user device 630may communicate with the transportation management system via a cellularnetwork and the Internet, but communicate with the vehicle 640 viaBluetooth or a physical wire connection.

In particular embodiments, the transportation management system 660 mayfulfill ride requests for one or more users 601 by dispatching suitablevehicles. The transportation management system 660 may receive anynumber of ride requests from any number of ride requestors 601. Inparticular embodiments, a ride request from a ride requestor 601 mayinclude an identifier that identifies the ride requestor in the system660. The transportation management system 660 may use the identifier toaccess and store the ride requestor's 601 information, in accordancewith the requestor's 601 privacy settings. The ride requestor's 601information may be stored in one or more data stores (e.g., a relationaldatabase system) associated with and accessible to the transportationmanagement system 660. In particular embodiments, ride requestorinformation may include profile information about a particular riderequestor 601. In particular embodiments, the ride requestor 601 may beassociated with one or more categories or types, through which the riderequestor 601 may be associated with aggregate information about certainride requestors of those categories or types. Ride information mayinclude, for example, preferred pick-up and drop-off locations, drivingpreferences (e.g., safety comfort level, preferred speed, rates ofacceleration/deceleration, safety distance from other vehicles whentravelling at various speeds, route, etc.), entertainment preferencesand settings (e.g., preferred music genre or playlist, audio volume,display brightness, etc.), temperature settings, whether conversationwith the driver is welcomed, frequent destinations, historical ridingpatterns (e.g., time of day of travel, starting and ending locations,etc.), preferred language, age, gender, or any other suitableinformation. In particular embodiments, the transportation managementsystem 660 may classify a user 601 based on known information about theuser 601 (e.g., using machine-learning classifiers), and use theclassification to retrieve relevant aggregate information associatedwith that class. For example, the system 660 may classify a user 601 asa young adult and retrieve relevant aggregate information associatedwith young adults, such as the type of music generally preferred byyoung adults.

Transportation management system 660 may also store and access rideinformation. Ride information may include locations related to the ride,traffic data, route options, optimal pick-up or drop-off locations forthe ride, or any other suitable information associated with a ride. Asan example and not by way of limitation, when the transportationmanagement system 660 receives a request to travel from San FranciscoInternational Airport (SFO) to Palo Alto, Calif., the system 660 mayaccess or generate any relevant ride information for this particularride request. The ride information may include, for example, preferredpick-up locations at SFO; alternate pick-up locations in the event thata pick-up location is incompatible with the ride requestor (e.g., theride requestor may be disabled and cannot access the pick-up location)or the pick-up location is otherwise unavailable due to construction,traffic congestion, changes in pick-up/drop-off rules, or any otherreason; one or more routes to navigate from SFO to Palo Alto; preferredoff-ramps for a type of user; or any other suitable informationassociated with the ride. In particular embodiments, portions of theride information may be based on historical data associated withhistorical rides facilitated by the system 660. For example, historicaldata may include aggregate information generated based on past rideinformation, which may include any ride information described herein andtelemetry data collected by sensors in vehicles and user devices.Historical data may be associated with a particular user (e.g., thatparticular user's preferences, common routes, etc.), a category/class ofusers (e.g., based on demographics), and all users of the system 660.For example, historical data specific to a single user may includeinformation about past rides that particular user has taken, includingthe locations at which the user is picked up and dropped off, music theuser likes to listen to, traffic information associated with the rides,time of the day the user most often rides, and any other suitableinformation specific to the user. As another example, historical dataassociated with a category/class of users may include, e.g., common orpopular ride preferences of users in that category/class, such asteenagers preferring pop music, ride requestors who frequently commuteto the financial district may prefer to listen to the news, etc. As yetanother example, historical data associated with all users may includegeneral usage trends, such as traffic and ride patterns. Usinghistorical data, the system 660 in particular embodiments may predictand provide ride suggestions in response to a ride request. Inparticular embodiments, the system 660 may use machine-learning, such asneural networks, regression algorithms, instance-based algorithms (e.g.,k-Nearest Neighbor), decision-tree algorithms, Bayesian algorithms,clustering algorithms, association-rule-learning algorithms,deep-learning algorithms, dimensionality-reduction algorithms, ensemblealgorithms, and any other suitable machine-learning algorithms known topersons of ordinary skill in the art. The machine-learning models may betrained using any suitable training algorithm, including supervisedlearning based on labeled training data, unsupervised learning based onunlabeled training data, and semi-supervised learning based on a mixtureof labeled and unlabeled training data.

In particular embodiments, transportation management system 660 mayinclude one or more server computers. Each server may be a unitaryserver or a distributed server spanning multiple computers or multipledatacenters. The servers may be of various types, such as, for exampleand without limitation, web server, news server, mail server, messageserver, advertising server, file server, application server, exchangeserver, database server, proxy server, another server suitable forperforming functions or processes described herein, or any combinationthereof. In particular embodiments, each server may include hardware,software, or embedded logic components or a combination of two or moresuch components for carrying out the appropriate functionalitiesimplemented or supported by the server. In particular embodiments,transportation management system 660 may include one or more datastores. The data stores may be used to store various types ofinformation, such as ride information, ride requestor information, rideprovider information, historical information, third-party information,or any other suitable type of information. In particular embodiments,the information stored in the data stores may be organized according tospecific data structures. In particular embodiments, each data store maybe a relational, columnar, correlation, or any other suitable type ofdatabase system. Although this disclosure describes or illustratesparticular types of databases, this disclosure contemplates any suitabletypes of databases. Particular embodiments may provide interfaces thatenable a user device 630 (which may belong to a ride requestor orprovider), a transportation management system 660, vehicle system 640,or a third-party system 670 to process, transform, manage, retrieve,modify, add, or delete the information stored in the data store.

In particular embodiments, transportation management system 660 mayinclude an authorization server (or any other suitable component(s))that allows users 601 to opt-in to or opt-out of having theirinformation and actions logged, recorded, or sensed by transportationmanagement system 660 or shared with other systems (e.g., third-partysystems 670). In particular embodiments, a user 601 may opt-in oropt-out by setting appropriate privacy settings. A privacy setting of auser may determine what information associated with the user may belogged, how information associated with the user may be logged, wheninformation associated with the user may be logged, who may loginformation associated with the user, whom information associated withthe user may be shared with, and for what purposes informationassociated with the user may be logged or shared. Authorization serversmay be used to enforce one or more privacy settings of the users 601 oftransportation management system 660 through blocking, data hashing,anonymization, or other suitable techniques as appropriate.

In particular embodiments, third-party system 670 may be anetwork-addressable computing system that may provide HD maps or hostGPS maps, customer reviews, music or content, weather information, orany other suitable type of information. Third-party system 670 maygenerate, store, receive, and send relevant data, such as, for example,map data, customer review data from a customer review website, weatherdata, or any other suitable type of data. Third-party system 670 may beaccessed by the other computing entities of the network environmenteither directly or via network 610. For example, user device 630 mayaccess the third-party system 670 via network 610, or via transportationmanagement system 660. In the latter case, if credentials are requiredto access the third-party system 670, the user 601 may provide suchinformation to the transportation management system 660, which may serveas a proxy for accessing content from the third-party system 670.

In particular embodiments, user device 630 may be a mobile computingdevice such as a smartphone, tablet computer, or laptop computer. Userdevice 630 may include one or more processors (e.g., CPU, GPU), memory,and storage. An operating system and applications may be installed onthe user device 630, such as, e.g., a transportation applicationassociated with the transportation management system 660, applicationsassociated with third-party systems 670, and applications associatedwith the operating system. User device 630 may include functionality fordetermining its location, direction, or orientation, based on integratedsensors such as GPS, compass, gyroscope, or accelerometer. User device630 may also include wireless transceivers for wireless communicationand may support wireless communication protocols such as Bluetooth,near-field communication (NFC), infrared (IR) communication, WI-FI, and2G/3G/4G/LTE mobile communication standard. User device 630 may alsoinclude one or more cameras, scanners, touchscreens, microphones,speakers, and any other suitable input-output devices.

In particular embodiments, the vehicle 640 may be equipped with an arrayof sensors 644, a navigation system 646, and a ride-service computingdevice 648. In particular embodiments, a fleet of vehicles 640 may bemanaged by the transportation management system 660. The fleet ofvehicles 640, in whole or in part, may be owned by the entity associatedwith the transportation management system 660, or they may be owned by athird-party entity relative to the transportation management system 660.In either case, the transportation management system 660 may control theoperations of the vehicles 640, including, e.g., dispatching selectvehicles 640 to fulfill ride requests, instructing the vehicles 640 toperform select operations (e.g., head to a service center orcharging/fueling station, pull over, stop immediately, self-diagnose,lock/unlock compartments, change music station, change temperature, andany other suitable operations), and instructing the vehicles 640 toenter select operation modes (e.g., operate normally, drive at a reducedspeed, drive under the command of human operators, and any othersuitable operational modes).

In particular embodiments, the vehicles 640 may receive data from andtransmit data to the transportation management system 660 and thethird-party system 670. Examples of received data may include, e.g.,instructions, new software or software updates, maps, 3D models, trainedor untrained machine-learning models, location information (e.g.,location of the ride requestor, the vehicle 640 itself, other vehicles640, and target destinations such as service centers), navigationinformation, traffic information, weather information, entertainmentcontent (e.g., music, video, and news) ride requestor information, rideinformation, and any other suitable information. Examples of datatransmitted from the vehicle 640 may include, e.g., telemetry and sensordata, determinations/decisions based on such data, vehicle condition orstate (e.g., battery/fuel level, tire and brake conditions, sensorcondition, speed, odometer, etc.), location, navigation data, passengerinputs (e.g., through a user interface in the vehicle 640, passengersmay send/receive data to the transportation management system 660 andthird-party system 670), and any other suitable data.

In particular embodiments, vehicles 640 may also communicate with eachother, including those managed and not managed by the transportationmanagement system 660. For example, one vehicle 640 may communicate withanother vehicle data regarding their respective location, condition,status, sensor reading, and any other suitable information. Inparticular embodiments, vehicle-to-vehicle communication may take placeover direct short-range wireless connection (e.g., WI-FI, Bluetooth,NFC) or over a network (e.g., the Internet or via the transportationmanagement system 660 or third-party system 670), or both.

In particular embodiments, a vehicle 640 may obtain and processsensor/telemetry data. Such data may be captured by any suitablesensors. For example, the vehicle 640 may have a Light Detection andRanging (LiDAR) sensor array of multiple LiDAR transceivers that areconfigured to rotate 360°, emitting pulsed laser light and measuring thereflected light from objects surrounding vehicle 640. In particularembodiments, LiDAR transmitting signals may be steered by use of a gatedlight valve, which may be a MEMs device that directs a light beam usingthe principle of light diffraction. Such a device may not use a gimbaledmirror to steer light beams in 360° around the vehicle. Rather, thegated light valve may direct the light beam into one of several opticalfibers, which may be arranged such that the light beam may be directedto many discrete positions around the vehicle. Thus, data may becaptured in 360° around the vehicle, but no rotating parts may benecessary. A LiDAR is an effective sensor for measuring distances totargets, and as such may be used to generate a three-dimensional (3D)model of the external environment of the vehicle 640. As an example andnot by way of limitation, the 3D model may represent the externalenvironment including objects such as other cars, curbs, debris,objects, and pedestrians up to a maximum range of the sensor arrangement(e.g., 50, 100, or 200 meters). As another example, the vehicle 640 mayhave optical cameras pointing in different directions. The cameras maybe used for, e.g., recognizing roads, lane markings, street signs,traffic lights, police, other vehicles, and any other visible objects ofinterest. To enable the vehicle 640 to “see” at night, infrared camerasmay be installed. In particular embodiments, the vehicle may be equippedwith stereo vision for, e.g., spotting hazards such as pedestrians ortree branches on the road. As another example, the vehicle 640 may haveradars for, e.g., detecting other vehicles and hazards afar.Furthermore, the vehicle 640 may have ultrasound equipment for, e.g.,parking and obstacle detection. In addition to sensors enabling thevehicle 640 to detect, measure, and understand the external world aroundit, the vehicle 640 may further be equipped with sensors for detectingand self-diagnosing the vehicle's own state and condition. For example,the vehicle 640 may have wheel sensors for, e.g., measuring velocity;global positioning system (GPS) for, e.g., determining the vehicle'scurrent geolocation; and inertial measurement units, accelerometers,gyroscopes, and odometer systems for movement or motion detection. Whilethe description of these sensors provides particular examples ofutility, one of ordinary skill in the art would appreciate that theutilities of the sensors are not limited to those examples. Further,while an example of a utility may be described with respect to aparticular type of sensor, it should be appreciated that the utility maybe achieved using any combination of sensors. For example, the vehicle640 may build a 3D model of its surrounding based on data from itsLiDAR, radar, sonar, and cameras, along with a pre-generated mapobtained from the transportation management system 660 or thethird-party system 670. Although sensors 644 appear in a particularlocation on the vehicle 640 in FIG. 6, sensors 644 may be located in anysuitable location in or on the vehicle 640. Example locations forsensors include the front and rear bumpers, the doors, the frontwindshield, on the side panel, or any other suitable location.

In particular embodiments, the vehicle 640 may be equipped with aprocessing unit (e.g., one or more CPUs and GPUs), memory, and storage.The vehicle 640 may thus be equipped to perform a variety ofcomputational and processing tasks, including processing the sensordata, extracting useful information, and operating accordingly. Forexample, based on images captured by its cameras and a machine-visionmodel, the vehicle 640 may identify particular types of objects capturedby the images, such as pedestrians, other vehicles, lanes, curbs, andany other objects of interest.

In particular embodiments, the vehicle 640 may have a navigation system646 responsible for safely navigating the vehicle 640. In particularembodiments, the navigation system 646 may take as input any type ofsensor data from, e.g., a Global Positioning System (GPS) module,inertial measurement unit (IMU), LiDAR sensors, optical cameras, radiofrequency (RF) transceivers, or any other suitable telemetry or sensorymechanisms. The navigation system 646 may also utilize, e.g., map data,traffic data, accident reports, weather reports, instructions, targetdestinations, and any other suitable information to determine navigationroutes and particular driving operations (e.g., slowing down, speedingup, stopping, swerving, etc.). In particular embodiments, the navigationsystem 646 may use its determinations to control the vehicle 640 tooperate in prescribed manners and to guide the vehicle 640 to itsdestinations without colliding into other objects. Although the physicalembodiment of the navigation system 646 (e.g., the processing unit)appears in a particular location on the vehicle 640 in FIG. 6,navigation system 646 may be located in any suitable location in or onthe vehicle 640. Example locations for navigation system 646 includeinside the cabin or passenger compartment of the vehicle 640, near theengine/battery, near the front seats, rear seats, or in any othersuitable location.

In particular embodiments, the vehicle 640 may be equipped with aride-service computing device 648, which may be a tablet or any othersuitable device installed by transportation management system 660 toallow the user to interact with the vehicle 640, transportationmanagement system 660, other users 601, or third-party systems 670. Inparticular embodiments, installation of ride-service computing device648 may be accomplished by placing the ride-service computing device 648inside the vehicle 640, and configuring it to communicate with thevehicle 640 via a wired or wireless connection (e.g., via Bluetooth).Although FIG. 6 illustrates a single ride-service computing device 648at a particular location in the vehicle 640, the vehicle 640 may includeseveral ride-service computing devices 648 in several differentlocations within the vehicle. As an example and not by way oflimitation, the vehicle 640 may include four ride-service computingdevices 648 located in the following places: one in front of thefront-left passenger seat (e.g., driver's seat in traditional U.S.automobiles), one in front of the front-right passenger seat, one infront of each of the rear-left and rear-right passenger seats. Inparticular embodiments, ride-service computing device 648 may bedetachable from any component of the vehicle 640. This may allow usersto handle ride-service computing device 648 in a manner consistent withother tablet computing devices. As an example and not by way oflimitation, a user may move ride-service computing device 648 to anylocation in the cabin or passenger compartment of the vehicle 640, mayhold ride-service computing device 648, or handle ride-service computingdevice 648 in any other suitable manner. Although this disclosuredescribes providing a particular computing device in a particularmanner, this disclosure contemplates providing any suitable computingdevice in any suitable manner.

FIG. 7 illustrates an example computer system 700. In particularembodiments, one or more computer systems 700 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 700 provide thefunctionalities described or illustrated herein. In particularembodiments, software running on one or more computer systems 700performs one or more steps of one or more methods described orillustrated herein or provides the functionalities described orillustrated herein. Particular embodiments include one or more portionsof one or more computer systems 700. Herein, a reference to a computersystem may encompass a computing device, and vice versa, whereappropriate. Moreover, a reference to a computer system may encompassone or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems700. This disclosure contemplates computer system 700 taking anysuitable physical form. As example and not by way of limitation,computer system 700 may be an embedded computer system, a system-on-chip(SOC), a single-board computer system (SBC) (such as, for example, acomputer-on-module (COM) or system-on-module (SOM)), a desktop computersystem, a laptop or notebook computer system, an interactive kiosk, amainframe, a mesh of computer systems, a mobile telephone, a personaldigital assistant (PDA), a server, a tablet computer system, anaugmented/virtual reality device, or a combination of two or more ofthese. Where appropriate, computer system 700 may include one or morecomputer systems 700; be unitary or distributed; span multiplelocations; span multiple machines; span multiple data centers; or residein a cloud, which may include one or more cloud components in one ormore networks. Where appropriate, one or more computer systems 700 mayperform without substantial spatial or temporal limitation one or moresteps of one or more methods described or illustrated herein. As anexample and not by way of limitation, one or more computer systems 700may perform in real time or in batch mode one or more steps of one ormore methods described or illustrated herein. One or more computersystems 700 may perform at different times or at different locations oneor more steps of one or more methods described or illustrated herein,where appropriate.

In particular embodiments, computer system 700 includes a processor 702,memory 704, storage 706, an input/output (I/O) interface 708, acommunication interface 710, and a bus 712. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 702 includes hardware for executinginstructions, such as those making up a computer program. As an exampleand not by way of limitation, to execute instructions, processor 702 mayretrieve (or fetch) the instructions from an internal register, aninternal cache, memory 704, or storage 706; decode and execute them; andthen write one or more results to an internal register, an internalcache, memory 704, or storage 706. In particular embodiments, processor702 may include one or more internal caches for data, instructions, oraddresses. This disclosure contemplates processor 702 including anysuitable number of any suitable internal caches, where appropriate. Asan example and not by way of limitation, processor 702 may include oneor more instruction caches, one or more data caches, and one or moretranslation lookaside buffers (TLBs). Instructions in the instructioncaches may be copies of instructions in memory 704 or storage 706, andthe instruction caches may speed up retrieval of those instructions byprocessor 702. Data in the data caches may be copies of data in memory704 or storage 706 that are to be operated on by computer instructions;the results of previous instructions executed by processor 702 that areaccessible to subsequent instructions or for writing to memory 704 orstorage 706; or any other suitable data. The data caches may speed upread or write operations by processor 702. The TLBs may speed upvirtual-address translation for processor 702. In particularembodiments, processor 702 may include one or more internal registersfor data, instructions, or addresses. This disclosure contemplatesprocessor 702 including any suitable number of any suitable internalregisters, where appropriate. Where appropriate, processor 702 mayinclude one or more arithmetic logic units (ALUs), be a multi-coreprocessor, or include one or more processors 702. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 704 includes main memory for storinginstructions for processor 702 to execute or data for processor 702 tooperate on. As an example and not by way of limitation, computer system700 may load instructions from storage 706 or another source (such asanother computer system 700) to memory 704. Processor 702 may then loadthe instructions from memory 704 to an internal register or internalcache. To execute the instructions, processor 702 may retrieve theinstructions from the internal register or internal cache and decodethem. During or after execution of the instructions, processor 702 maywrite one or more results (which may be intermediate or final results)to the internal register or internal cache. Processor 702 may then writeone or more of those results to memory 704. In particular embodiments,processor 702 executes only instructions in one or more internalregisters or internal caches or in memory 704 (as opposed to storage 706or elsewhere) and operates only on data in one or more internalregisters or internal caches or in memory 704 (as opposed to storage 706or elsewhere). One or more memory buses (which may each include anaddress bus and a data bus) may couple processor 702 to memory 704. Bus712 may include one or more memory buses, as described in further detailbelow. In particular embodiments, one or more memory management units(MMUs) reside between processor 702 and memory 704 and facilitateaccesses to memory 704 requested by processor 702. In particularembodiments, memory 704 includes random access memory (RAM). This RAMmay be volatile memory, where appropriate. Where appropriate, this RAMmay be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 704 may include one ormore memories 704, where appropriate. Although this disclosure describesand illustrates particular memory, this disclosure contemplates anysuitable memory.

In particular embodiments, storage 706 includes mass storage for data orinstructions. As an example and not by way of limitation, storage 706may include a hard disk drive (HDD), a floppy disk drive, flash memory,an optical disc, a magneto-optical disc, magnetic tape, or a UniversalSerial Bus (USB) drive or a combination of two or more of these. Storage706 may include removable or non-removable (or fixed) media, whereappropriate. Storage 706 may be internal or external to computer system700, where appropriate. In particular embodiments, storage 706 isnon-volatile, solid-state memory. In particular embodiments, storage 706includes read-only memory (ROM). Where appropriate, this ROM may bemask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM),electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM),or flash memory or a combination of two or more of these. Thisdisclosure contemplates mass storage 706 taking any suitable physicalform. Storage 706 may include one or more storage control unitsfacilitating communication between processor 702 and storage 706, whereappropriate. Where appropriate, storage 706 may include one or morestorages 706. Although this disclosure describes and illustratesparticular storage, this disclosure contemplates any suitable storage.

In particular embodiments, I/O interface 708 includes hardware orsoftware, or both, providing one or more interfaces for communicationbetween computer system 700 and one or more I/O devices. Computer system700 may include one or more of these I/O devices, where appropriate. Oneor more of these I/O devices may enable communication between a personand computer system 700. As an example and not by way of limitation, anI/O device may include a keyboard, keypad, microphone, monitor, mouse,printer, scanner, speaker, still camera, stylus, tablet, touch screen,trackball, video camera, another suitable I/O device or a combination oftwo or more of these. An I/O device may include one or more sensors.This disclosure contemplates any suitable I/O devices and any suitableI/O interfaces 708 for them. Where appropriate, I/O interface 708 mayinclude one or more device or software drivers enabling processor 702 todrive one or more of these I/O devices. I/O interface 708 may includeone or more I/O interfaces 708, where appropriate. Although thisdisclosure describes and illustrates a particular I/O interface, thisdisclosure contemplates any suitable I/O interface.

In particular embodiments, communication interface 710 includes hardwareor software, or both providing one or more interfaces for communication(such as, for example, packet-based communication) between computersystem 700 and one or more other computer systems 700 or one or morenetworks. As an example and not by way of limitation, communicationinterface 710 may include a network interface controller (NIC) ornetwork adapter for communicating with an Ethernet or any otherwire-based network or a wireless NIC (WNIC) or wireless adapter forcommunicating with a wireless network, such as a WI-FI network. Thisdisclosure contemplates any suitable network and any suitablecommunication interface 710 for it. As an example and not by way oflimitation, computer system 700 may communicate with an ad hoc network,a personal area network (PAN), a local area network (LAN), a wide areanetwork (WAN), a metropolitan area network (MAN), or one or moreportions of the Internet or a combination of two or more of these. Oneor more portions of one or more of these networks may be wired orwireless. As an example, computer system 700 may communicate with awireless PAN (WPAN) (such as, for example, a Bluetooth WPAN), a WI-FInetwork, a WI-MAX network, a cellular telephone network (such as, forexample, a Global System for Mobile Communications (GSM) network), orany other suitable wireless network or a combination of two or more ofthese. Computer system 700 may include any suitable communicationinterface 710 for any of these networks, where appropriate.Communication interface 710 may include one or more communicationinterfaces 710, where appropriate. Although this disclosure describesand illustrates a particular communication interface, this disclosurecontemplates any suitable communication interface.

In particular embodiments, bus 712 includes hardware or software, orboth coupling components of computer system 700 to each other. As anexample and not by way of limitation, bus 712 may include an AcceleratedGraphics Port (AGP) or any other graphics bus, an Enhanced IndustryStandard Architecture (EISA) bus, a front-side bus (FSB), aHYPERTRANSPORT (HT) interconnect, an Industry Standard Architecture(ISA) bus, an INFINIBAND interconnect, a low-pin-count (LPC) bus, amemory bus, a Micro Channel Architecture (MCA) bus, a PeripheralComponent Interconnect (PCI) bus, a PCI-Express (PCIe) bus, a serialadvanced technology attachment (SATA) bus, a Video Electronics StandardsAssociation local (VLB) bus, or another suitable bus or a combination oftwo or more of these. Bus 712 may include one or more buses 712, whereappropriate. Although this disclosure describes and illustrates aparticular bus, this disclosure contemplates any suitable bus orinterconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other types of integratedcircuits (ICs) (such, as for example, field-programmable gate arrays(FPGAs) or application-specific ICs (ASICs)), hard disk drives (HDDs),hybrid hard drives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A or B, or both,” unless expressly indicated otherwise orindicated otherwise by context. Moreover, “and” is both joint andseveral, unless expressly indicated otherwise or indicated otherwise bycontext. Therefore, herein, “A and B” means “A and B, jointly orseverally,” unless expressly indicated otherwise or indicated otherwiseby context.

Methods described herein may vary in accordance with the presentdisclosure. Various embodiments of this disclosure may repeat one ormore steps of the methods described herein, where appropriate. Althoughthis disclosure describes and illustrates particular steps of certainmethods as occurring in a particular order, this disclosure contemplatesany suitable steps of the methods occurring in any suitable order or inany combination which may include all, some, or none of the steps of themethods. Furthermore, although this disclosure may describe andillustrate particular components, devices, or systems carrying outparticular steps of a method, this disclosure contemplates any suitablecombination of any suitable components, devices, or systems carrying outany suitable steps of the method.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, modules,elements, feature, functions, operations, or steps, any of theseembodiments may include any combination or permutation of any of thecomponents, modules, elements, features, functions, operations, or stepsdescribed or illustrated anywhere herein that a person having ordinaryskill in the art would comprehend. Furthermore, reference in theappended claims to an apparatus or system or a component of an apparatusor system being adapted to, arranged to, capable of, configured to,enabled to, operable to, or operative to perform a particular functionencompasses that apparatus, system, component, whether or not it or thatparticular function is activated, turned on, or unlocked, as long asthat apparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

What is claimed is:
 1. A computer-implemented method comprising: determining, by a computing system, map information defining a map, wherein the map comprises a plurality of regions; assigning, by the computing system, a quality level to each region of the plurality of regions based on map information available for that region, wherein the quality level is associated with at least one of: a resolution metric, a volume metric, a recency metric, a verification metric, or an elegance metric associated with the map information available for that region; identifying, by the computing system, a first region of the plurality of regions that is at risk of being downgraded to a lower quality level; and issuing, by the computing system, instructions to one or more vehicles that cause the one or more vehicles to traverse the first region and capture sensor data within the first region.
 2. The computer-implemented method of claim 1, further comprising: receiving, from the one or more vehicles, input data pertaining to the first region; and updating map information associated with the first region based on the input data.
 3. The computer-implemented method of claim 2, further comprising updating the quality level for the first region based on the updated map information.
 4. The computer-implemented method of claim 2, wherein the input data comprises sensor data captured by one or more sensors mounted to the one or more vehicles while driving through the first region.
 5. The computer-implemented method of claim 2, further comprising: transmitting updated map information to one or more vehicles based on the updating the map information associated with the first region.
 6. The computer-implemented method of claim 1, wherein the first region is assigned a first quality level of a plurality of quality levels, and further wherein the first quality level is indicative of AV-quality map information that would permit operation of an autonomous vehicle within the first region.
 7. The computer-implemented method of claim 6, further comprising assigning a rideshare request to an autonomous vehicle based on the first region being assigned the first quality level of the plurality of quality levels.
 8. The computer-implemented method of claim 1, wherein the issuing instructions to the one or more vehicles that cause the one or more vehicles to traverse the first region and capture sensor data within the first region comprises issuing instructions that cause one or more autonomous vehicles to traverse the first region and capture sensor data within the first region.
 9. The computer-implemented method of claim 1, further comprising: receiving input data pertaining to the first region, wherein the input data is indicative of a change to one or more map elements within the first region; and downgrading the quality level assigned to the first region based on the input data.
 10. The computer-implemented method of claim 1, wherein the assigning the quality level to each region of the plurality of regions based on map information available for that region comprises: assigning one quality level of a plurality of quality levels to each region of the plurality of regions, wherein each quality level of the plurality of quality levels is associated with a set of quality criteria, and each region is assigned the highest quality level for which the associated set of quality criteria is satisfied.
 11. A system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to perform: determining map information defining a map, wherein the map comprises a plurality of regions; assigning a quality level to each region of the plurality of regions based on map information available for that region, wherein the quality level is associated with at least one of: a resolution metric, a volume metric, a recency metric, a verification metric, or an elegance metric associated with the map information available for that region; identifying a first region of the plurality of regions that is at risk of being downgraded to a lower quality level; and issuing instructions to one or more vehicles that cause the one or more vehicles to traverse the first region and capture sensor data within the first region.
 12. The system of claim 11, wherein the instructions, when executed by the at least one processor, further cause the system to perform: receiving, from the one or more vehicles, input data pertaining to the first region; and updating map information associated with the first region based on the input data.
 13. The system of claim 12, wherein the instructions, when executed by the at least one processor, further cause the system to perform: updating the quality level for the first region based on the updated map information.
 14. The system of claim 12, wherein the input data comprises sensor data captured by one or more sensors mounted to the one or more vehicles while driving through the first region.
 15. The system of claim 12, wherein the instructions, when executed by the at least one processor, further cause the system to perform: transmitting updated map information to one or more vehicles based on the updating the map information associated with the first region.
 16. A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising: determining map information defining a map, wherein the map comprises a plurality of regions; assigning a quality level to each region of the plurality of regions based on map information available for that region, wherein the quality level is associated with at least one of: a resolution metric, a volume metric, a recency metric, a verification metric, or an elegance metric associated with the map information available for that region; identifying a first region of the plurality of regions that is at risk of being downgraded to a lower quality level; and issuing instructions to one or more vehicles that cause the one or more vehicles to traverse the first region and capture sensor data within the first region.
 17. The non-transitory computer-readable storage medium of claim 16, wherein the instructions, when executed by the at least one processor of the computing system, further cause the computing system to perform: receiving, from the one or more vehicles, input data pertaining to the first region; and updating map information associated with the first region based on the input data.
 18. The non-transitory computer-readable storage medium of claim 17, wherein the instructions, when executed by the at least one processor of the computing system, further cause the computing system to perform: updating the quality level for the first region based on the updated map information.
 19. The non-transitory computer-readable storage medium of claim 17, wherein the input data comprises sensor data captured by one or more sensors mounted to the one or more vehicles while driving through the first region.
 20. The non-transitory computer-readable storage medium of claim 17, wherein the instructions, when executed by the at least one processor of the computing system, further cause the computing system to perform: transmitting updated map information to one or more vehicles based on the updating the map information associated with the first region. 